Medical images of a patient's body are generally taken by a technician using an imaging modality, such as an x-ray machine or a magnetic resonance (MR) imaging device. At a single imaging session, one or more diagnostic images of one or more body parts may be taken and recorded. Such diagnostic images may then be entered into a picture archiving and communication system (PACS) for later access by a medical practitioner. PACS systems generally store and transmit data in accordance with the Digital Imaging and Communications in Medicine (DICOM) international standard. Imaging modalities, such as MR and other diagnostic imaging devices, generally communicate directly with the PACS over a network using DICOM. The function of the PACS is to maintain a database of diagnostic images taken on connected devices along with related information for image display and patient demographics.
In addition to the image data, the DICOM records include demographic information such as patient identification information, accession number, the start and end times of tests, and may include identification of the attending staff member(s). Privacy concerns, including laws, regulations and institutional policies, strictly limit access to such personally identifiable information so that generally only specifically authorized medical personnel within a medical facility can access such image data with associated DICOM records.
Often research teams in, for example, universities, teaching hospitals, companies performing research and development into new technologies, and pharmaceuticals companies would like to access PACS clinical data in order to perform research studies. However, privacy issues make it difficult to transport PACS data out of the clinical arena due to privacy regulations and rules. To do so requires largely a manual process of de-identifying the data set which is a costly and error-prone endeavour especially when the data sets involved are fairly large, e.g. more than 1000 images per study.
Once the data sets are de-identified, there is no standard way of mapping the fictitious identity back to the original identity so that longitudinal or multi-timepoint studies can be done. Also, once the data sets are de-identified, there is no standard way of transporting the data sets out of the clinical setting in an efficient and secure way
Very mature research teams may use a PACS purchased from a traditional clinical PACS vendor to store their research images. Such PACS systems are underpowered because they must adhere to the DICOM data model with Patient-Study-Series entities at the root. Thus, the ability to organize data sets in ways that make the most sense for any particular research project does not exist; one must conform to the DICOM data model. Because of the DICOM data model and the standard for searching, your ability to search for data of relevance is limited in most cases to a Patient or Study identifier. Once many data sets have been stored in the PACS, it is very difficult to make use of this data again, because it is difficult to find the data that would be of interest to the researcher. Once in a traditional clinical PACS, it is also very difficult to retrieve the data in a programmatic way. The DICOM standard assumes an interactive user interface for searches and retrieves. Programmatic and automated retrieval is desirable for research teams because they will often want to retrieve data, run particular processing or analysis algorithms on the data, and then send the output data back into the data store.
Use of a traditional clinical PACS is also problematic in research because such archives will only store DICOM-formatted data, usually with limited support for arbitrarily formatted data files, which is the case with almost all instances of data output from processing or analysis operations.
The use of a traditional clinical PACS does not provide a unified product and tools development platform which leads to a great deal of waste in redundant efforts. For example, at the SPIE Medical Imaging conference held in February 2013, multiple papers were presented that documented projects involving novel image processing or analysis approaches and algorithms all requiring an image viewer of some kind. Every project produced their own, ad hoc image viewer software, even projects from within the same academic institution.